• Science of Emotion and Sensibility
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• v.13 no.1
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• pp.47-60
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• 2010

Most of the researches about classification usually have used kNN(k-Nearest Neighbor), SVM(Support Vector Machine), which are known as learn-based model, and Bayesian classifier, NNA(Neural Network Algorithm), which are known as statistics-based methods. However, there are some limitations of space and time when classifying so many web pages in recent internet. Moreover, most studies of classification are using uni-gram feature representation which is not good to represent real meaning of words. In case of Korean web page classification, there are some problems because of korean words property that the words have multiple meanings(polysemy). For these reasons, LSA(Latent Semantic Analysis) is proposed to classify well in these environment(large data set and words' polysemy). LSA uses SVD(Singular Value Decomposition) which decomposes the original term-document matrix to three different matrices and reduces their dimension. From this SVD's work, it is possible to create new low-level semantic space for representing vectors, which can make classification efficient and analyze latent meaning of words or document(or web pages). Although LSA is good at classification, it has some drawbacks in classification. As SVD reduces dimensions of matrix and creates new semantic space, it doesn't consider which dimensions discriminate vectors well but it does consider which dimensions represent vectors well. It is a reason why LSA doesn't improve performance of classification as expectation. In this paper, we propose new LSA which selects optimal dimensions to discriminate and represent vectors well as minimizing drawbacks and improving performance. This method that we propose shows better and more stable performance than other LSAs' in low-dimension space. In addition, we derive more improvement in classification as creating and selecting features by reducing stopwords and weighting specific values to them statistically.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• Proceedings of the Korean Nutrition Society Conference
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• 1995.11b
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• pp.11-34
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• 1995

Growth hormone (GH) plays a key role in regulating postnatal growth and can stimulate growth of animals by acting directly on specific receptors on the plasma membrane of tissues or indirectly through stimulating insulin-like growth factor (IGF)-I synthesis and secretion by the liver and other tissues. IGF-I and IGF-Ⅱ are polypeptides with structural similarity with proinsulin that stimulate cell proliferation by endocrine, paracrine and autocrine mechanisms. The initial event in the metabolic action of IGFs on target cells appears to be their binding to specific receptors on the plasma membrane. Current evidence indicates that the mitogenic actions of both IGFs are mediated primarily by binding to the type I IGF receptors, and that IGF action is also mediated by interactions with IGF-binding proteins (IGFBPs). Six distinct IGFBPs have been identified that are characterized by cell-specific interaction, transcriptional and post-translational regulation by many different effectors, and the ability to either potentiate or inhibit IGF actions. Nutritional deficiencies can have their devastating consequence during growth. Although IGF-I is the major mediator of GH's action on somatic growth, nutritional status of an organism is a critical regulator of IGF-I and IGFBPs. Various nutrient deficiencies result in decreased serum IGF-I levels and altered IGFBP levels, but the blood levels of GH are generally unchanged or elevated in malnutrition. Effects of protein, energy, vitamin C and D, and zinc on serum IGF and IGFBP levels and tissue mRNA levels were reviewed in the text. Multiple factors are involved in the regulation of intestinal epithelial cell growth and differentiation. Among these factors the nutritional status of individuals is the most important. The intestinal epithelium is an important site for mitogenic action of the IGFs in vivo, with exogenous IGF-I stimulating mucosal hyperplasia. Therefore, the IGF system appears to provide and important mechanism linking nutrition and the proliferation of intestinal epithelial cells. In order to study the detailed mechanisms by which intestinal mucosa is regulated, we have utilized IEC-6 cells, an intestinal epithelial cell line and Caco-2 cells, a human colon adenocarcinoma cell line. Like intestinal crypt cells analyzed in vivo or freshly isolated intestinal epithelial cells, IEC-6 cells and Caco-2 cells possess abundant quatities of both type Ⅰ and type Ⅱ IGF receptors. Exogenous IGFs stimulate, whereas addition of IGFBP-2 inhibits IEC-6 cell proliferation. To investigate whether endogenously secreted IGFBP-2 inhibit proliferation, IEC-6 cells were transfected with a full-length rat IGFBP-2 cDNA anti-sense expression construct. IEC-6 cells transfected with anti-sense IGFBP-2 protein in medium. These cells grew at a rate faster than the control cells indicating that endogenous IGFBP-2 inhibits proliferation of IEC-6 cells, probably by sequestering IGFs. IEC-6 cells express many characteristics of enterocyte, but do not undergo differentiation. On the other hand, Caco-2 cells undergo a spontaneous enterocyte differentiation. On the other hand, Caco-2 cells undergo a spontaneous enterocyte differentiation after reaching confluency. We have demonstrated that Caco-2 cells produce IGF-Ⅱ, IGFBP-2, IGFBP-3, and an as yet unidentified 31,000 Mr IGFBP, and that both mRNA and peptide secretion of IGFBP-2 and IGFBP-3 increased, but IGFBP-4 mRNA and protein secretion decreased after the cells reached confluency. These changes occurred in parallel to and were coincident with differentiation of the cells, as measured by expression of sucrase-isomaltase. In addition, Caco-2 cell clones forced to overexpress IGFBP-4 by transfection with a rat IGFBP-4 cDNA construct exhibited a significantly slower growth rate under serum-free conditions and had increased expression of sucrase-isomaltase compared with vector control cells. These results indicate that IGFBP-4 inhibits proliferation and stimulates differentiation of Caco-2 cells, probably by inhibiting the mitogenic actions of IGFs.